基于模糊积分和粒子群算法的多分类器融合

使用模糊积分的方法将多个分类器进行融合可以提高分类精度,但是如何得到最优的模糊测度是一个尚未解决的问题。本文根据模糊测度Sugeno积分的理想特性,用模糊测度代替各个分类器的权值,利用粒子群算法全局搜索的优势,将模糊测度对应于粒子,并随速度和位置并不断调整,从而得到全局最优的模糊测度。通过仿真实例验证了新的多分类器融合模型具有较低的分类错误率,并能有效地提高分类精度。     

基于模糊积分和生态捕食模型的多分类器融合

考虑到各个独立分类器之间的重要程度和相互作用,根据模糊测度Sugeno积分的理想特性,用模糊测度代替各个分类器的权值,利用捕食-被捕食模型动态周期性变化的特点,将模糊测度对应于种群规模并不断随之进行调整.通过仿真实例证实了新的多分类器融合模型具有较低的分类错误率,能够有效地提高分类精度.     

基于模糊积分和遗传算法的分类器组合算法

将多个分类器进行组合能提高分类精度。基于模糊测度的Sugeno和Choquet积分具有理想的特性,因此该文利用其进行分类器组合。然而在实际中难以求得模糊测度。该文利用两种方法求取模糊测度,一是分类器对样本数据的分类能力,另一种是根据遗传算法。这两种方法均考虑了每个分类器对不同类的分类能力不同这一经验知识。实验中对UCI中的几个数据库进行了测试,同时将该组合方法应用于一多传感器融合工件识别系统。测试结果表明了该算法是一种计算简便、精度较高的分类器组合方法。     

基于模糊积分分类器融合的人脸识别

提出一种基于模糊积分分类器融合的人脸识别算法。对人脸图像进行小波变换,选取合适的小波基函数及有效的分解层数,提取低频分量系数作为分类特征设计分类器。对原图像采用2DPCA进行特征提取设计另一分类器,采用模糊积分的算法融合2个分类器并得出最终分类识别结果。实验结果表明,模糊积分能够有效融合分类器的互补信息,提高系统的分类性能,从而提高人脸识别率。     

基于图卷积神经网络的自注意力的融合节点分类框架

图神经网络因其强大的建模能力引起广泛关注, 常常被用来解决图上的节点分类任务. 现阶段常用的以图卷积神经网络 (graph convolutional network, GCN)为内核的模型解决此类问题, 但往往因为出现过拟合与过平滑而导致深层的节点嵌入表示效果并不好. 因此, 本文提出了一种基于GCN内核的结合残差连接与自注意力方法——GCNRN模型, 以提升GCN的泛化能力. 同时, 为了整合更深入的信息, 本文引入融合机制, 采用模糊积分融合多个分类器, 最终提高模型测试精度. 为了验证所提出方法的优越性, 本文采用ogbn-arxiv与常用的引文数据集进行了对比实验. GCNRN模型与多个以GCN为内核的现有模型相比, 节点分类准确率平均提高了2%, 且避免了传统的过拟合和过平滑现象. 此外, 实验结果表明, 增加了基于模糊积分的融合模块的多分类器模型比传统融合方法具有更好的分类效果.     

分类器融合中模糊积分理论研究进展

将模糊积分理论与多分类器融合方法中其他两种研究热点方法进行了比较.介绍模糊积分的基本模型及通用求解过程.讨论目前分类器融合领域模糊积分理论的几个研究方向的发展现状和面临的问题,分析模糊积分理论基本模型和几种扩展模型的求解算法.提出了分类器融合领域模糊积分理论未来理论和应用研究中的开放课题.     

基于模糊积分的多神经网络集成信息融合

使用模糊积分实现集成神经网络中的子分类器信息融合,提出一种更加有效和全面的模糊密度,用于模糊积分的计算。以双螺旋分类问题为实验对象,使用集成神经网络实现具有较高正确率的分类方法,对神经网络集成的有效性和各类参数的设定作实验分析,并通过多种模糊密度的比较数据说明该模糊密度函数的有效性。     

表面肌电信息融合与动作分类

提出基于多个特征域信息融合的方法,进一步去除不确定性、提高表面肌电分类准确率.选择的表面肌电特征参数分别为时域绝对值积分、AR模型系数和线性倒谱系数.待辨识的6类手部动作肌电信号经各特征域变换,提取特征矢量后由BP神经网络分类,根据D-S证据理论对各分类器分类结果进行证据累积,并得到最终分类结果.实验结果表明,动作分类准确率高于传统的单特征集单分类器的分类方法,且训练、分类效率高于结构化神经网络特征融合方法.     

多决策树的模糊积分融合在银行信贷管理系统中的应用

为了提高基于数据挖掘的商业银行信贷管理系统的信贷风险评估水平,将多决策树的Choquet模糊积分融合(MTCFF)模型应用到银行信贷管理系统中。基本思想是采用决策树在已知类型的客户数据上进行挖掘,按照决策树剪枝程度不同形成不同的决策树并产生规则,利用所生成的不同决策树的规则,对未知类型的客户数据进行分类,然后让Choquet模糊积分对多棵决策树的分类结果进行融合,形成最优判断。采用UCI数据库中German客户信用卡数据集进行验证,实验证明Choquet模糊积分的非线性融合效果优于单棵决策树的分类效果,也优于其他线性融合方法,并且Choquet模糊积分要优于Sugeno模糊积分。     

多决策树的模糊积分融合在银行信贷管理系统中的应用

为了提高基于数据挖掘的商业银行信贷管理系统的信贷风险评估水平,将多决策树的Choquet模糊积分融合(MTCFF)模型应用到银行信贷管理系统中。基本思想是采用决策树在已知类型的客户数据上进行挖掘,按照决策树剪枝程度不同形成不同的决策树并产生规则,利用所生成的不同决策树的规则,对未知类型的客户数据进行分类,然后让Choquet模糊积分对多棵决策树的分类结果进行融合,形成最优判断。采用UCI数据库中German客户信用卡数据集进行验证,实验证明Choquet模糊积分的非线性融合效果优于单棵决策树的分类效果,也优于其他线性融合方法,并且Choquet模糊积分要优于Sugeno模糊积分。     

基于遗传算法和模糊积分的多分类器集成

多分类器联合是解决复杂模式识别问题的有效办法。模糊积分是其中一种多分类器联合方法。但是对于模糊积分。如何计算模糊积分密度是一个尚未解决的问题。本文提出了一种基于模糊积分和遗传算法的分类器集成方法，该方法利用遗传算法计算模糊积分密度函数，再利用模糊积分把分类器输出信息联合起来。实验结果表明，该方法比其他方法能够得到更好的识别性能。     

基于分类器联合的表情识别

提出了一种基于分类器联合的人脸表情识别方法。首先采用CKFD算法在双决策子空间中提取两类表情特征并融合;分别利用最近邻、最小距离和神经网络三种子分类器进行识别;最后运用模糊积分对子分类器的识别结果进行融合。基于JAFFE的实验结果表明,它是一种有效的表情识别方法。     

Face recognition using fuzzy Integral and wavelet decomposition method

In this paper, we develop a method for recognizing face images by combining wavelet decomposition, Fisherface method, and fuzzy integral. The proposed approach is comprised of four main stages. The first stage uses the wavelet decomposition that helps extract intrinsic features of face images. As a result of this decomposition, we obtain four subimages (namely approximation, horizontal, vertical, and diagonal detailed images). The second stage of the approach concerns the application of the Fisherface method to these four decompositions. The choice of the Fisherface method in this setting is motivated by its insensitivity to large variation in light direction, face pose, and facial expression. The two last phases are concerned with the aggregation of the individual classifiers by means of the fuzzy integral. Both Sugeno and Choquet type of fuzzy integral are considered as the aggregation method. In the experiments we use n-fold cross-validation to assure high consistency of the produced classification outcomes. The experimental results obtained for the Chungbuk National University (CNU) and Yale University face databases reveal that the approach presented in this paper yields better classification performance in comparison to the results obtained by other classifiers.     

Detection of heart valve diseases by using fuzzy discrete hidden Markov model

In the present study, biomedical based application was developed to classify the data belongs to normal and abnormal samples generated by Doppler ultrasound. This study consists of raw data obtaining and pre-processing, feature extraction and classification steps. In the pre-processing step, a high-pass filter, white de-noising and normalization were used. During the feature extraction step, wavelet entropy was applied by wavelet transform and short time fourier transform. Obtained features were classified by fuzzy discrete hidden Markov model (FDHMM). For this purpose, a FDHMM that consists of Sugeno and Choquet integrals and λ fuzzy measurement was defined to eliminate statistical dependence assumptions to increase the performance and to have better flexibility. Moreover, Sugeno integral was used together with triangular norms that are mentioned frequently in the literature in order to increase the performance. Experimental results show that recognition rate obtained by Sugeno fuzzy integral with triangular norm is more successful than recognition rates obtained by standard discrete HMM (DHMM) and Choquet integral based FDHMM. In addition to this, it is shown in this study that the performance of the Sugeno integral based method is better than the performances of artificial neural network (ANN) and HMM based classification systems that were used in previous studies of the authors.     

Confidence factor weighted Gaussian function induced parallel fuzzy rank-level fusion for inference and its application to face recognition

This paper proposes a novel approach for inference using fuzzy rank-level fusion and explores it application to face recognition using multiple biometric representations. Multiple representations of single biometric (trait) aim to increase the reliability or acceptance of a biometric system, as it exploits the underlying essential characteristics provided by different sensors. In this paper, we propose a new scheme for generating fuzzy ranks induced by a Gaussian function based on the confidence of a classifier. In contrast to the conventional ranking, this fuzzy ranking reflects some associations among the outputs (confidence factors) of a classifier. These fuzzy ranks, yielded by multiple representations of a face image, are fused weighted by the corresponding confidence factors of the classifier to generate the final ranks while recognizing a face. In many real-world applications, where multiple traits of a person are unavailable, the proposed method is highly effective. However, it can easily be extended to multimodal biometric systems utilizing multiple classifiers. The experimental results using different feature vectors of a face image employing different classifiers show that the proposed method can significantly improve recognition accuracy as compared to those from individual feature vectors and as well as some commonly used rank-level fusion methods.     

基于模糊积分的多分类器组合的入侵检测

将模式识别的方法应用到入侵检测系统中解决了传统的基于规则的入侵检测方法的缺陷。提出了一种基于模糊积分的多分类器组合的入侵检测方法,并提出了一种模糊密度的计算方法,通过KDD’99中的入侵检测数据的实验证明,该方法检测准确性较高。